Project: Vehicle-Detection¶
In [1]:
# Load helper function from yolo_utils.py
from utils.yolo_utils import *
1. Load test images¶
In [2]:
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
%matplotlib inline
# Plot the images in the specific folder
def visualize(filename, a):
fig, axes = plt.subplots(2,3,figsize=(24,12),subplot_kw={'xticks':[],'yticks':[]})
fig.subplots_adjust(hspace=0.03, wspace=0.05)
for p in zip(sum(axes.tolist(),[]), a):
p[0].imshow(p[1],cmap='gray')
plt.tight_layout()
fig.savefig(filename)
plt.show()
return p[1].shape
In [3]:
import glob
import matplotlib.image as mpimg
image_shape = visualize("output_images/test_images.jpg",
(mpimg.imread(f) for f in (glob.glob("test_images/test*.jpg"))))
print("Image size: " + str(image_shape))
2. Filtering with threshold on class scores¶
In [4]:
import tensorflow as tf
from keras import backend as K
def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold=.6):
"""Filters YOLO boxes by thresholding on object and class confidence.
Arguments:
box_confidence -- tensor of shape (19, 19, 5, 1)
boxes -- tensor of shape (19, 19, 5, 4)
box_class_probs -- tensor of shape (19, 19, 5, 80)
threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box
Returns:
scores -- tensor of shape (None,), containing the class probability score for selected boxes
boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes
classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes
Note: "None" is here because you don't know the exact number of selected boxes, as it depends on the threshold.
For example, the actual output size of scores would be (10,) if there are 10 boxes.
"""
# Step 1: Compute box scores
box_scores = tf.multiply(box_class_probs, box_confidence)
# Step 2: Find the box_classes thanks to the max box_scores, keep track of the corresponding score
box_classes = K.argmax(box_scores, axis=-1)
box_class_scores = K.max(box_scores, axis=-1)
# Step 3: Create a filtering mask based on "box_class_scores" by using "threshold". The mask should have the
# same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)
filtering_mask = box_class_scores > threshold
# Step 4: Apply the mask to scores, boxes and classes
scores = tf.boolean_mask(box_class_scores, filtering_mask)
boxes = tf.boolean_mask(boxes, filtering_mask)
classes = tf.boolean_mask(box_classes, filtering_mask)
return scores, boxes, classes
3. Non-max Suppression: NMS¶
3.1 Intersection over Union: IoU¶
In [5]:
def iou(box1, box2):
"""Implement the intersection over union (IoU) between box1 and box2
Arguments:
box1 -- first box, list object with coordinates (x1, y1, x2, y2)
box2 -- second box, list object with coordinates (x1, y1, x2, y2)
"""
# Calculate the (y1, x1, y2, x2) coordinates of the intersection of box1 and box2. Calculate its Area.
xi1 = max(box1[0], box2[0])
yi1 = max(box1[1], box2[1])
xi2 = min(box1[2], box2[2])
yi2 = min(box1[3], box2[3])
inter_area = (xi2 - xi1) * (yi2 - yi1)
# Calculate the Union area by using Formula: Union(A,B) = A + B - Inter(A,B)
box1_area = (box1[2] - box1[0]) * (box1[3] - box1[1])
box2_area = (box2[2] - box2[0]) * (box2[3] - box2[1])
union_area = box1_area + box2_area - inter_area
# compute the IoU
iou = float(inter_area) / union_area
return iou
In [6]:
def yolo_non_max_suppression(scores, boxes, classes, max_boxes=10, iou_threshold=0.5):
"""
Applies Non-max suppression (NMS) to set of boxes
Arguments:
scores -- tensor of shape (None,), output of yolo_filter_boxes()
boxes -- tensor of shape (None, 4), output of yolo_filter_boxes() that have been scaled to the image size (see later)
classes -- tensor of shape (None,), output of yolo_filter_boxes()
max_boxes -- integer, maximum number of predicted boxes you'd like
iou_threshold -- real value, "intersection over union" threshold used for NMS filtering
Returns:
scores -- tensor of shape (, None), predicted score for each box
boxes -- tensor of shape (4, None), predicted box coordinates
classes -- tensor of shape (, None), predicted class for each box
Note: The "None" dimension of the output tensors has obviously to be less than max_boxes. Note also that this
function will transpose the shapes of scores, boxes, classes. This is made for convenience.
"""
# tensor to be used in tf.image.non_max_suppression()
max_boxes_tensor = K.variable(max_boxes, dtype='int32')
# initialize variable max_boxes_tensor
K.get_session().run(tf.variables_initializer([max_boxes_tensor]))
# Use tf.image.non_max_suppression() to get the list of indices corresponding to boxes you keep
nms_indices = tf.image.non_max_suppression(boxes=boxes, scores=scores, max_output_size=max_boxes,
iou_threshold=iou_threshold)
# Use K.gather() to select only nms_indices from scores, boxes and classes
scores = K.gather(scores, nms_indices)
boxes = K.gather(boxes, nms_indices)
classes = K.gather(classes, nms_indices)
return scores, boxes, classes
4. Wrapping up the filtering¶
In [7]:
def yolo_eval(yolo_outputs, image_shape=(720., 1280.), max_boxes=10, score_threshold=.5, iou_threshold=.5):
"""
Converts the output of YOLO encoding (a lot of boxes) to your predicted boxes along with their scores, box coordinates and classes.
Arguments:
yolo_outputs -- output of the encoding model (for image_shape of (608, 608, 3)), contains 4 tensors:
box_confidence: tensor of shape (None, 19, 19, 5, 1)
box_xy: tensor of shape (None, 19, 19, 5, 2)
box_wh: tensor of shape (None, 19, 19, 5, 2)
box_class_probs: tensor of shape (None, 19, 19, 5, 80)
image_shape -- tensor of shape (2,) containing the input shape, in this notebook we use (608., 608.) (has to be float32 dtype)
max_boxes -- integer, maximum number of predicted boxes you'd like
score_threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box
iou_threshold -- real value, "intersection over union" threshold used for NMS filtering
Returns:
scores -- tensor of shape (None, ), predicted score for each box
boxes -- tensor of shape (None, 4), predicted box coordinates
classes -- tensor of shape (None,), predicted class for each box
"""
# Retrieve outputs of the YOLO model
box_xy, box_wh, box_confidence, box_class_probs = yolo_outputs
# Convert boxes to be ready for filtering functions
boxes = yolo_boxes_to_corners(box_xy, box_wh)
# Use one of the functions implemented to perform Score-filtering with a threshold of score_threshold
scores, boxes, classes = yolo_filter_boxes(box_class_probs=box_class_probs, box_confidence=box_confidence,
boxes=boxes)
# Scale boxes back to original image shape.
boxes = scale_boxes(boxes, image_shape)
# Use one of the functions you've implemented to perform Non-max suppression with a threshold of iou_threshold (≈1 line)
scores, boxes, classes = yolo_non_max_suppression(boxes=boxes, classes=classes, scores=scores)
return scores, boxes, classes
5. Test on Images¶
5.1 Defining classes, anchors and image shape¶
In [8]:
# TXT includes 80 classes information
class_names = read_classes("model_data/coco_classes.txt")
# TXT includes 5 boxes
anchors = read_anchors("model_data/yolo_anchors.txt")
# Define image shape
image_shape = (720., 1280.)
5.2 Load pretrained model¶
In [9]:
from keras.models import load_model, Model
yolo_model = load_model('model_data/yolo.h5')
In [10]:
yolo_model.summary()
5.3 Convert output of the model to usable bounding box tensors¶
In [11]:
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
5.4 Filter boxes¶
In [12]:
scores, boxes, all_classes = yolo_eval(yolo_outputs, image_shape)
5.5 Run on images¶
In [13]:
def predict(sess, image, printlabel, scores, boxes, classes, class_names):
"""
Runs the graph stored in "sess" to predict boxes for "image_file". Prints and plots the preditions.
Arguments:
sess -- your tensorflow/Keras session containing the YOLO graph
image_file -- name of an image stored in the "images" folder.
Returns:
out_scores -- tensor of shape (None, ), scores of the predicted boxes
out_boxes -- tensor of shape (None, 4), coordinates of the predicted boxes
out_classes -- tensor of shape (None, ), class index of the predicted boxes
Note: "None" actually represents the number of predicted boxes, it varies between 0 and max_boxes.
"""
# Preprocess your image
image, image_data = preprocess_image(image, model_image_size = (608, 608))
# Run the session with the correct tensors and choose the correct placeholders in the feed_dict.
# need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})
out_scores, out_boxes, out_classes = sess.run(
[scores, boxes, classes],
feed_dict={yolo_model.input:image_data, K.learning_phase():0})
colors = generate_colors(class_names)
# Draw bounding boxes on the image file
draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors=colors, printlabel=printlabel)
return out_scores, out_boxes, out_classes, image
In [14]:
sess = K.get_session()
In [15]:
import PIL
img_path = glob.glob('test_images/*')
for f in img_path:
image = PIL.Image.open(f)
out_scores, out_boxes, out_classes, output_image = predict(sess,image,True,scores,boxes,all_classes,class_names=class_names)
print('Found {} boxes for {}'.format(len(out_boxes), f))
fig=plt.figure(figsize=(20, 20))
plt.imshow(output_image)
plt.show()
6. Put them together¶
In [16]:
import numpy as np
def pipline(img):
img = PIL.Image.fromarray(img)
out_scores, out_boxes, out_classes, output_image = predict(sess,img,False,scores,boxes,all_classes,class_names=class_names)
return np.array(output_image)
7. Run on video¶
In [17]:
from moviepy.editor import VideoFileClip
video_output = 'project_video_output.mp4'
clip1 = VideoFileClip("project_video.mp4")
img_clip = clip1.fl_image(pipline) #NOTE: this function expects color images!!
%time img_clip.write_videofile(video_output, audio=False)
In [18]:
from IPython.display import HTML
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(video_output))
Out[18]: